import numpy as np
from common.functions import *
from common.util import im2col,col2im

class Relu:
    def __init__(self):
        self.mask = None 

    def forward(self,x):
        self.mask = (x<=0)
        out = x.copy()
        out[self.mask] = 0
        return out 
    
    def backward(self,dout):
        dout[self.mask] = 0
        dx = dout 

        return dx 
    
class Sigmoid:
    def __init__(self):
        self.out = None

    def forward(self,x):
        out = sigmoid(x)
        self.out = out 
        return out 
    
    def backward(self,dout):
        dx = dout*(1.0-self.out)*self.out 
        return dx 
    
class Affine:
    def __init__(self,W,b):
        self.W = W 
        self.b = b

        self.x = None 
        self.original_x_shape = None 
        self.dW = None 
        self.db = None 

    def forward(self,x):
        # 对应张量
        self.original_x_shape = x.shape
        x = x.reshape(x.shape[0],-1)
        self.x = x 

        out = np.dot(self.x,self.W) + self.b
        return out 
    
    def backward(self,dout):
        dx = np.dot(dout,self.W.T)
        self.dW = np.dot(self.x.T, dout)
        self.db = np.sum(dout,axis=0)

        dx = dx.reshape(*self.original_x_shape)
        return dx 
    
class SoftmaxWithLoss:
    def __init__(self):
        self.loss = None 
        self.y = None # softmax输出
        self.t = None # 监督数据

    def forward(self,x,t):
        self.t = t 
        self.y = softmax(x)
        self.loss = cross_entropy_error(self.y,self.t)

        return self.loss
    
    def backward(self,dout=1):
        batch_size = self.t.shape[0]
        if self.t.size == self.y.size: # 监督数据one-hot-vector形式
            dx = (self.y-self.t)/batch_size
        else:
            dx = self.y.copy()
            dx[np.arange(batch_size),self.t] -= 1
            dx = dx/batch_size
        
        return dx 
    
class Dropout:
    """
    http://arxiv.org/abs/1207.0580
    """
    def __init__(self, dropout_ratio=0.5):
        self.dropout_ratio = dropout_ratio
        self.mask = None

    def forward(self, x, train_flg=True):
        if train_flg:
            self.mask = np.random.rand(*x.shape) > self.dropout_ratio
            return x * self.mask
        else:
            return x * (1.0 - self.dropout_ratio)

    def backward(self, dout):
        return dout * self.mask
    
class BatchNormalization:
    """
    http://arxiv.org/abs/1502.03167
    """
    def __init__(self,gamma,beta,momentum=0.9,running_mean=None,running_var=None):
        self.gamma = gamma
        self.beta = beta 
        self.momentum = momentum 
        self.input_shape = None 

        # 测试时使用的平均值和方差
        self.running_mean = running_mean
        self.running_var = running_var 

        # backward时使用的中间数据
        self.batch_size = None 
        self.xc = None 
        self.std = None 
        self.dgamma = None 
        self.dbeta = None 

    def forward(self,x,train_flg=True):
        self.input_shape = x.shape
        if x.ndim != 2:
            N,C,H,W = x.shape
            x = x.reshape(N,-1)

        out = self.__forward(x,train_flg)
        return out.reshape(*self.input_shape)

    def __forward(self,x,train_flg):
        if self.running_mean is None:
            N,D = x.shape
            self.running_mean = np.zeros(D)
            self.running_var = np.zeros(D)

        if train_flg:
            mu = x.mean(axis=0)
            xc = x - mu 
            var = np.mean(xc**2,axis=0)
            std = np.sqrt(var+10e-7)
            xn = xc/std 

            self.batch_size = x.shape[0]
            self.xc = xc 
            self.xn = xn 
            self.std = std 
            self.running_mean = self.momentum*self.running_mean+(1-self.momentum)*mu
            self.running_var = self.momentum*self.running_var+(1-self.momentum)*var 

        else:
            xc = x - self.running_mean
            xn = xc/(np.sqrt(self.running_var + 10e-7))

        out = self.gamma*xn +self.beta 
        return out 
    
    def backward(self,dout):
        if dout.ndim != 2:
            N,C,H,W = dout.shape
            dout = dout.reshape(N,-1)

        dx = self.__backward(dout)

        dx = dx.reshape(*self.input_shape)
        return dx 

    def __backward(self,dout):
        dbeta = dout.sum(axis=0)
        dgamma = np.sum(self.xn*dout,axis=0)

        dxn = self.gamma*dout 
        dxc = dxn/self.std 
        dstd = -np.sum((dxn*self.xc)/(self.std*self.std),axis=0)
        dvar = 0.5*dstd/self.std 
        dxc += (2.0/self.batch_size)*self.xc*dvar
        dmu = np.sum(dxc,axis=0)
        dx = dxc-dmu/self.batch_size

        self.dgamma = dgamma
        self.dbeta = dbeta

        return dx 
    
# 卷积层
class Convolution:
    def __init__(self,W,b,stride=1,pad=0):
        self.W = W # 滤波器（权重）
        self.b = b # 偏置
        self.stride = stride # 步幅
        self.pad = pad  # 填充 

        # 中间数据（backward时使用）
        self.x = None 
        self.col = None 
        self.col_W = None 

        # 权重和偏置参数的梯度
        self.dW = None 
        self.db = None 

    def forward(self,x):
        FN,C,FH,FW = self.W.shape
        N,C,H,W = x.shape

        # 根据输入和过滤器，计算输出的形状
        out_h = 1+int((H+2*self.pad-FH)/self.stride)
        out_w = 1 +int((W+2*self.pad -FW)/self.stride)

        col = im2col(x,FH,FW,self.stride,self.pad)
        col_W = self.W.reshape(FN,-1).T

        out = np.dot(col,col_W)+self.b
        out = out.reshape(N,out_h,out_w,-1).transpose(0,3,1,2)

        self.x = x
        self.col = col
        self.col_W = col_W

        return out 
    
    def backward(self,dout):
        FN,C,FH,FW = self.W.shape
        dout = dout.transpose(0,2,3,1).reshape(-1,FN)

        self.db = np.sum(dout,axis=0)
        self.dW = np.dot(self.col.T,dout)
        self.dW = self.dW.transpose(1,0).reshape(FN,C,FH,FW)

        dcol = np.dot(dout,self.col_W.T)
        dx = col2im(dcol,self.x.shape,FH,FW,self.stride,self.pad)

        return dx 
    

# 池化层
class Pooling:
    def __init__(self, pool_h, pool_w, stride=1, pad=0):
        self.pool_h = pool_h
        self.pool_w = pool_w
        self.stride = stride
        self.pad = pad
        
        self.x = None
        self.arg_max = None

    def forward(self, x):
        N, C, H, W = x.shape
        out_h = int(1 + (H - self.pool_h) / self.stride)
        out_w = int(1 + (W - self.pool_w) / self.stride)

        col = im2col(x, self.pool_h, self.pool_w, self.stride, self.pad)
        col = col.reshape(-1, self.pool_h*self.pool_w)

        arg_max = np.argmax(col, axis=1)
        out = np.max(col, axis=1)
        out = out.reshape(N, out_h, out_w, C).transpose(0, 3, 1, 2)

        self.x = x
        self.arg_max = arg_max

        return out

    def backward(self, dout):
        dout = dout.transpose(0, 2, 3, 1)
        
        pool_size = self.pool_h * self.pool_w
        dmax = np.zeros((dout.size, pool_size))
        dmax[np.arange(self.arg_max.size), self.arg_max.flatten()] = dout.flatten()
        dmax = dmax.reshape(dout.shape + (pool_size,)) 
        
        dcol = dmax.reshape(dmax.shape[0] * dmax.shape[1] * dmax.shape[2], -1)
        dx = col2im(dcol, self.x.shape, self.pool_h, self.pool_w, self.stride, self.pad)
        
        return dx